/external/webp/src/enc/ |
analysis_enc.c | 77 const int centers[NUM_MB_SEGMENTS], 80 int min = centers[0], max = centers[0]; 85 if (min > centers[n]) min = centers[n]; 86 if (max < centers[n]) max = centers[n]; 92 const int alpha = 255 * (centers[n] - mid) / (max - min); 93 const int beta = 255 * (centers[n] - min) / (max - min); 146 // array bounds of 'centers' with some compilers (noticed with gcc-4.9) 149 int centers[NUM_MB_SEGMENTS]; local [all...] |
/external/tensorflow/tensorflow/core/kernels/ |
clustering_ops.cc | 298 InvalidArgument("Input centers should be a matrix.")); 316 const Eigen::Map<const MatrixXfRowMajor> centers( 339 0.5 * centers.rowwise().squaredNorm(); 343 // sharding the points and centers as follows: 345 // 1. Centers are sharded such that each block of centers has at most 348 // block of centers. The block size of points is chosen such that the 352 // are reduced to a set of k nearest centers as soon as possible. This 369 num_centers) /* centers in a block */ 372 // The memory needed for storing the centers being processed. This is share [all...] |
clustering_ops_test.cc | 35 // Number of centers for tests. 179 Tensor centers(DT_FLOAT, TensorShape({num_centers, num_dims})); 182 centers.flat<float>().setRandom(); 187 .Input(test::graph::Constant(g, centers))
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/external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
gmm_ops_test.py | 46 self.centers = [[1, 1], [-1, 0.5], [2, 1]] 48 self.num_examples, self.centers) 73 def make_data_from_centers(num_vectors, centers): 74 """Generates 2-dimensional data with random centers. 78 centers: a list of random 2-dimensional centers. 86 current_class = np.random.random_integers(0, len(centers) - 1) 88 np.random.normal(centers[current_class][0], 90 np.random.normal(centers[current_class][1], np.random.random_sample()) 93 return np.asarray(vectors), len(centers) [all...] |
kmeans_test.py | 61 def make_random_points(centers, num_points, max_offset=20): 62 num_centers, num_dims = centers.shape 66 return (centers[assignments] + offsets, assignments, np.add.reduce( 221 # Make a call to fit to initialize the cluster centers. 381 centers = normalize(self.kmeans.cluster_centers()) 382 centers = centers[centers[:, 0].argsort()] 384 self.assertAllClose(centers, true_centers, atol=0.04) 388 centers = normalize(self.kmeans.cluster_centers() [all...] |
gmm_test.py | 78 def make_random_points(centers, num_points): 79 num_centers, num_dims = centers.shape 83 points = centers[assignments] + offsets
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/external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
kmeans_test.py | 61 def make_random_points(centers, num_points, max_offset=20): 62 num_centers, num_dims = centers.shape 66 return (centers[assignments] + offsets, assignments, np.add.reduce( 227 # Make a call to fit to initialize the cluster centers. 349 centers = normalize(self.kmeans.clusters()) 350 centers = centers[centers[:, 0].argsort()] 352 self.assertAllClose(centers, true_centers, atol=0.04) 356 centers = normalize(self.kmeans.clusters() [all...] |
dynamic_rnn_estimator_test.py | 661 # Create examples by choosing 'centers' and adding uniform noise. 662 centers = math_ops.matmul( 672 sequences = centers + noise
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/external/tensorflow/tensorflow/contrib/distribute/python/ |
keras_image_model_correctness_test.py | 63 centers = np.random.randn(num_classes, *shape) 72 features.append(centers[label] + offset)
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/external/skia/src/shaders/gradients/ |
SkTwoPointConicalGradient.cpp | 70 const SkPoint centers[2] = { c0 , c1 }; local 73 if (!gradientMatrix.setPolyToPoly(centers, unitvec, 2)) {
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/external/skqp/src/shaders/gradients/ |
SkTwoPointConicalGradient.cpp | 70 const SkPoint centers[2] = { c0 , c1 }; local 73 if (!gradientMatrix.setPolyToPoly(centers, unitvec, 2)) {
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/cts/apps/CtsVerifier/src/com/android/cts/verifier/sensors/ |
RVCVXCheckAnalyzer.java | 823 MatOfPoint2f centers = new MatOfPoint2f(); local 854 gray, patternSize, centers, Calib3d.CALIB_CB_ASYMMETRIC_GRID); 862 Calib3d.drawChessboardCorners(frame, patternSize, centers, true); 869 Calib3d.solvePnP(grid, centers, camMat, coeff, rvec, tvec, 884 Point[] centerPoints = centers.toArray(); 892 // Calculate the average pixel error between the circle centers from the video and the 893 // reprojected circle centers based on the estimated camera position. The error provides [all...] |
/external/libjpeg-turbo/simd/i386/ |
jdsample-avx2.asm | 44 ; The upsampling algorithm is linear interpolation between pixel centers, 46 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4 47 ; of the way between input pixel centers.
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jdsample-mmx.asm | 43 ; The upsampling algorithm is linear interpolation between pixel centers, 45 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4 46 ; of the way between input pixel centers.
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jdsample-sse2.asm | 43 ; The upsampling algorithm is linear interpolation between pixel centers, 45 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4 46 ; of the way between input pixel centers.
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/external/libjpeg-turbo/simd/x86_64/ |
jdsample-avx2.asm | 44 ; The upsampling algorithm is linear interpolation between pixel centers, 46 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4 47 ; of the way between input pixel centers.
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jdsample-sse2.asm | 43 ; The upsampling algorithm is linear interpolation between pixel centers, 45 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4 46 ; of the way between input pixel centers.
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/external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
metric_loss_ops_test.py | 411 # Expose cluster centers, i.e. medoids. 413 # Expose indices of chosen cluster centers. 521 n_samples=n_samples, centers=n_clusters)
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/external/tensorflow/tensorflow/go/op/ |
wrappers.go | [all...] |
/cts/apps/CtsVerifier/libs/ |
opencv3-android.jar | |